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A grid-based spatial index has the advantage that the structure of the index can be created first, and data added on an ongoing basis without requiring any change to the index structure; indeed, if a common grid is used by disparate data collecting and indexing activities, such indices can easily be merged from a variety of sources.
A spatial index is used by a spatial database to optimize spatial queries.Database systems use indices to quickly look up values by sorting data values in a linear (e.g. alphabetical) order; however, this way of indexing data is not optimal for spatial queries in two- or three-dimensional space.
R-trees are tree data structures used for spatial access methods, i.e., for indexing multi-dimensional information such as geographical coordinates, rectangles or polygons. The R-tree was proposed by Antonin Guttman in 1984 [2] and has found significant use in both theoretical and applied contexts. [3]
In data processing R*-trees are a variant of R-trees used for indexing spatial information. R*-trees have slightly higher construction cost than standard R-trees, as the data may need to be reinserted; but the resulting tree will usually have a better query performance. Like the standard R-tree, it can store both point and spatial data.
It is generally created within the constraints of chosen generic data models, so that existing GIS software can be used. For example, a data model for a city would include a list of data layers to be included (e.g., roads, buildings, parcels, zoning), with each being specified with the type of generic spatial data model being used (e.g. raster ...
This is a list of GIS data sources (including some geoportals) that provide information sets that can be used in geographic information systems (GIS) and spatial databases for purposes of geospatial analysis and cartographic mapping. This list categorizes the sources of interest.
The origin of the geodatabase was in the mid-1990s during the emergence of the first spatial databases.One early approach to integrating relational databases and GIS was the use of server middleware, a third-party program that stores the spatial data in database tables in a custom format, and translates it dynamically into a logical model that can be understood by the client software.
Spatial extract, transform, load (spatial ETL), also known as geospatial transformation and load (GTL), is a process for managing and manipulating geospatial data, for example map data. It is a type of extract, transform, load (ETL) process, with software tools and libraries specialised for geographical information.
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